Hierarchical data structure and method for prediction of tire wear

ABSTRACT

A computer-implemented method enables vehicle tire wear prediction based on minimal input data points. Information is aggregated in a hierarchical data structure regarding historical tread values for multiple tires, and respective values associated with the historical tread values for each of multiple parameters that are hierarchically defined from highest to lowest levels. A current tire tread value is provided from a sensor associated with a first tire, and respective values associated with the current tire tread value are provided for each of the multiple hierarchically defined parameters. The current tire tread value is matched with information from the hierarchical data structure corresponding to matching values for one or more of the hierarchically defined parameters having at least a predetermined threshold number of available tread values, and a tire wear rate is predicted for the first tire based at least in part on the matched information from the hierarchical data structure.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to tire wear prediction andmonitoring systems for wheeled vehicles.

More particularly, systems, methods, and related algorithms as disclosedherein may use hierarchical data structures and inferential statisticalmethods for, e.g., fleet management, cost forecasting, and improvedprediction of wear for tires of wheeled vehicles including but notlimited to motorcycles, consumer vehicles (e.g., passenger and lighttruck), commercial and off-road (OTR) vehicles.

BACKGROUND

Prediction of tire wear is an important tool for anyone owning oroperating vehicles, particularly in the context of fleet management. Astires are used, it is normal for the tread to gradually become shallowerand overall tire performance to change. At a certain point it becomescritical to be aware of the tire conditions, as insufficient tire treadcan create unsafe driving conditions. For example, when road conditionsare non-optimal the tires may be unable to grip the road and a drivermay lose control of his or her vehicle. Generally stated, the shallowerthe tire tread, the more easily the driver may lose traction whendriving in rain, snow, or the like.

In addition, irregular tread wear may occur for a variety of reasonsthat may lead users to replace a tire sooner than would otherwise havebeen necessary. Vehicles, drivers, and individual tires are alldifferent from each other, and can cause tires to wear at very differentrates. For instance, high performance tires for sports cars wear morequickly than touring tires for a family sedan. However, a wide varietyof factors can cause a tire to wear out sooner than expected, and/orcause it to wear irregularly and create noise or vibration. Two commoncauses of premature and/or irregular tire wear are improper inflationpressure and out-of-spec alignment conditions.

The estimation and/or prediction of tire wear over time may typicallyrequire knowledge of which tires are mounted in which wheel position fora given vehicle. However, most fleet management systems fail tosufficiently track or otherwise document such information. This createsdifficulties for a number of important fleet management tasks, such asfor example the generation of maintenance alerts, predicting the amountof wear life remaining, forecasting which (and when) tires will need tobe replaced, cost projections, etc.

Another issue is the length of time that is conventionally required toobtain meaningful wear information for a given tire. As one example inthe context of tire observation for waste vehicles, using a classiclinear regression model created at a tire level, it was determined thatsix to eight weeks of weekly tread depth measurements would be requiredfor reliable prediction of when a tire will be worn out. This amount oftime depends on how fast the tread wears out on a tire, and thereforethe expected window of time for monitoring tires in other businesssegments may be longer, as tires for other commercial tires maytypically wear at a slower rate. These temporal limitations inconventional methods for monitoring tread depth change are not ideal forwear prediction models.

BRIEF SUMMARY

A hierarchical modeling approach as disclosed herein may accurately andreliably enable tire wear prediction with less data points. Exemplarysuch methods may rely on populating distributions from meta datacollected with periodic wear inspections, such as e.g., wear informationabout a vehicle type, a vehicle type's axle, a specific vehicle, and aspecific vehicle's axle, all to better inform prediction about aspecific tire's wear rate. Fleet-level information and vehicle-levelinformation may be understood as populating faster than at the tirelevel. Furthermore, each measurement taken at the tire level influencesthe inference made using these different wear distributions.

Generally stated, a wear model as disclosed herein may enable the use ofpredicted wear rates to track performance of vehicles, specific tires,routes, drivers, and the like. Using the wear rate, a fleet manger canknow which trucks/driver/routes/tire models are burning through treadthe fastest, or conversely, saving tread. Furthermore, accurate wearmodeling helps a fleet plan tire purchasing. Wear-out prediction can forexample be aggregated into a projected ‘tire purchase estimation’ for agiven time period (e.g., year, month, week).

An exemplary embodiment of a computer-implemented method as disclosedherein for vehicle tire wear prediction may comprise aggregatinginformation in a hierarchical data structure regarding historical treadvalues for a plurality of tires, and respective values associated withthe historical tread values for each of a plurality of parameters thatare hierarchically defined from a highest level to a lowest level. Acurrent tire tread value may be provided from a sensor associated with afirst tire, and respective values associated with the current tire treadvalue for each of the plurality of hierarchically defined parameters maybe further provided, for example via a local computing device and/orassociated user interface. The current tire tread value may be matchedwith information from the hierarchical data structure corresponding tomatching values for one or more of the plurality of hierarchicallydefined parameters having at least a predetermined threshold number ofavailable tread values, wherein a tire wear rate is predicted for thefirst tire based at least in part on the matched information from thehierarchical data structure.

In an exemplary aspect of the aforementioned embodiment, the tire wearrate for the first tire may be predicted based at least in part on thematched information corresponding to a highest hierarchically definedparameter having at least the predetermined threshold number ofavailable tread values.

In another exemplary aspect of the aforementioned embodiment, thematched information from the highest hierarchically defined parametermay comprise a distribution of available tread values across a pluralityof zones, and the tire wear rate for the first tire may be predicted bymatching the current tire tread value with a subset of the distributionof tread values, and estimating future tire wear based on furtherhistorical information associated with the matched subset.

In another exemplary aspect of the aforementioned embodiment, treadvalues corresponding to the matched values for one or more of theplurality of hierarchically defined parameters may be filtered out ifsaid filtered tread values exceed a predetermined boundary, wherein thenumber of unfiltered tread values is compared to the predeterminedthreshold number of available tread values.

In another exemplary aspect of the aforementioned embodiment, theplurality of parameters may be selected based on relevance to tire wearstate prediction.

In another exemplary aspect of the aforementioned embodiment, theplurality of parameters may be hierarchically defined from a highestlevel to a lowest level based on fastest rates of population of thecorresponding information to slowest rates of population of thecorresponding information, respectively.

For example, the highest level of the hierarchically defined parametersmay be a vehicle fleet location, wherein the lowest level of thehierarchically defined parameters may be a particular wheel location fora particular vehicle, and intervening levels between the highest leveland the lowest level of the hierarchically defined parameters mayinclude one or more of a vehicle type, a vehicle axle type, and theparticular vehicle.

In another exemplary aspect of the aforementioned embodiment, areplacement time for the first tire may be predicted, based on thepredicted tire wear status, as compared with one or more tire wearthresholds associated with the first tire.

In another exemplary aspect of the aforementioned embodiment, the one ormore tire wear thresholds may comprise a tire tread thresholdcorresponding to a given wheel position associated with the first tire.

In another exemplary aspect of the aforementioned embodiment, a vehiclemaintenance alert comprising the predicted replacement time and anidentifier for the first tire may be generated and a message comprisingthe vehicle maintenance alert transmitted to a fleet management device.

An embodiment of a system for vehicle tire wear prediction may comprisea data storage network having a hierarchical data structure storedthereon, said hierarchical data structure aggregating informationregarding historical tread values for a plurality of tires, andrespective values associated with the historical tread values for eachof a plurality of parameters that are hierarchically defined from ahighest level to a lowest level. A sensor may be configured to provide acurrent tire tread value for at least a first tire, and at least onecomputing device is linked to the sensor and configured to furtherprovide respective values associated with the current tire tread valuefor each of the plurality of hierarchically defined parameters. Aserver-based computing network is configured to match the current tiretread value with information from the hierarchical data structurecorresponding to matching values for one or more of the plurality ofhierarchically defined parameters having at least a predeterminedthreshold number of available tread values, and predict a tire wear ratefor the first tire based at least in part on the matched informationfrom the hierarchical data structure.

The server-based computing network may likewise be configured to executeor otherwise direct the performance of various among the exemplaryaspect discussed above with respect to the aforementioned method.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Hereinafter, embodiments of the invention are illustrated in more detailwith reference to the drawings.

FIG. 1 is a block diagram representing an embodiment of a system fortire wear estimation as disclosed herein.

FIG. 2 is a graphical tree diagram representing an exemplary hierarchyof tire wear distributions moving from high variance to low variancedistributions.

FIG. 3 is a graphical diagram representing an exemplary node in the treediagram of FIG. 2 , including an aggregation of tire tread wearmeasurements with accompanying summary statistics persisted through datastorage.

FIG. 4 is a flowchart representing an exemplary embodiment of a methodas disclosed herein.

FIGS. 5A and 5B are portions of a collective flowchart representing anembodiment of a method as disclosed herein.

DETAILED DESCRIPTION

Referring generally to FIGS. 1-5B, various exemplary embodiments of aninvention may now be described in detail. Where the various figures maydescribe embodiments sharing various common elements and features withother embodiments, similar elements and features are given the samereference numerals and redundant description thereof may be omittedbelow.

Various embodiments of a system as disclosed herein may includecentralized computing nodes (e.g., a cloud server) in functionalcommunication with a plurality of distributed data collectors andcomputing nodes (e.g., associated with individual users and/or vehicles)for effectively implementing wear models as disclosed herein.

Referring initially to FIG. 1 , an exemplary embodiment of the system100 includes a tire tread depth sensor 120 configured to measure thetread depth for tires 110 that are targets for the wear models asdisclosed herein. The tire tread depth sensor 120 in various embodimentsmay be analog in nature, or may be a digital tread depth gauge, or maybe configured to scan the tire 110 and calculate a tread depth usinglaser, optical, imaging, or other equivalent sensing technologies as areknown to those of skill in the art. The tire tread depth sensor 120 maybe manually implemented, or it may be tire-mounted (e.g., mounteddirectly in the tire tread) or otherwise stationary and external to thetire. As one example, the tire tread depth sensor 120 may include adrive-over optical sensor comprising a laser emitter configured tocapture tire tread information by projecting laser light onto or acrossa surface of the tire 110 passing over the sensor 120, and one or morelaser receiving elements configured to capture reflected energy andthereby acquire a profile of the tire from which the tire tread may bedetermined.

A user can obtain the tread depth from the tire tread depth sensor 120and manually enter the information via a user interface 130 on a localcomputing device, which may for example be mobile or otherwise mountedonboard a vehicle, and configured to at least obtain data and transmitsaid data via a communications network to a remote server 140 and/orperform relevant computations as disclosed herein. The tread depthsensor 120 may be configured to automatically transmit the tread depthinformation for a given tire 110 to the user interface 130, wherein theuser for example can supplement the tread depth information with otherinformation associated with the tire 110, or may confirm data which ispopulated in the user interface 130 by the system 100 and alongside thereceived tread depth information.

The server 140 may in an embodiment be referred to as a hosted servernetwork, for example a private cloud server network and associatedcomputing, storage, and interface tools as would be understood by one ofskill in the art. The server-based functionality as disclosed herein mayaccordingly be implemented via one or more physical and/or virtualservers in a networked arrangement. However, in certain embodiments thefunctionality described herein with respect to the server may bedistributed among one or more physical and/or virtual servers in ahosted network and further for example among resident programapplications executed from local computing devices 130. The server 140is in functional communication with a data storage network such thatinformation may be selectively transmitted, stored, and retrieved forimplementation of methods as disclosed herein. For example, ahierarchical data model 142 as disclosed herein may be stored in adatabase or equivalent in the data storage network. Fleet data 144 mayalso be stored in a database or equivalent in the data storage network,such data including for example any information associated with therelevant vehicles or tires in a fleet that are outside the scope of themeta information stored in the hierarchical data model 142 but may bedesirable for users of the fleet management system or otherwiseimplemented by the system 100 for, e.g., establishing or confirmingrelationships between the nodes in the hierarchical data model.

A “data storage network” as used herein may refer generally toindividual, centralized, or distributed logical and/or physical entitiesconfigured to store data and enable selective retrieval of datatherefrom, and may include for example but without limitation a memory,look-up tables, files, registers, databases, and the like.

The models may be implemented at least in part via execution of aprocessor, enabling selective retrieval of, e.g., vehicle data and/ortire data and further in electronic communication for the input of anyadditional data or algorithms from a database, lookup table, or the likethat is stored in association with the server.

The system may include additional distributed program logic such as forexample residing on a fleet management server or other user computingdevice 150, or a user interface of a device resident to the vehicle orassociated with a driver thereof (not shown) for real-time notifications(e.g., via a visual and/or audio indicator), with the fleet managementdevice in some embodiments being functionally linked to the onboarddevice via a communications network. System programming information mayfor example be provided on-board by the driver or from a fleet manager.In certain embodiments the fleet management server or other usercomputing device 150 may be the same as the local computing device 130.

Referring next to FIG. 4 , an exemplary method 400 of tire wearestimation based on a hierarchical distribution model (as furtherillustrated in FIGS. 2 and 3 ) may now be described. A system asillustrated in FIG. 1 may be implemented, or alternative embodiments ofa system may be implemented for these or equivalent methods within thescope of the present disclosure unless otherwise stated. Depending onthe embodiment, certain acts, events, or functions of any of thealgorithms described herein can be performed in a different sequence,can be added, merged, or left out altogether (e.g., not all describedacts or events are necessary for the practice of the algorithm).

In an embodiment, the method 400 includes aggregating information in ahierarchical data structure regarding historical tread values for aplurality of tires, and respective values associated with the historicaltread values for each of a plurality of parameters that arehierarchically defined from a highest level to a lowest level (step410). A current tire tread value may be provided from a sensor 120associated with a first tire 110 (step 420), and respective valuesassociated with the current tire tread value for each of the pluralityof hierarchically defined parameters may be further provided (step 430),for example via a local computing device and/or associated userinterface 130. The current tire tread value may be matched withinformation from the hierarchical data structure corresponding tomatching values for one or more of the plurality of hierarchicallydefined parameters having at least a predetermined threshold number ofavailable tread values (step 440), wherein a tire wear rate is predictedfor the first tire based at least in part on the matched informationfrom the hierarchical data structure (step 450).

The tire wear rate for the first tire may for example be predicted basedat least in part on the matched information corresponding to a highesthierarchically defined parameter having at least the predeterminedthreshold number of available tread values.

The matched information from the highest hierarchically definedparameter may comprise a distribution of available tread values across aplurality of zones, and the tire wear rate for the first tire may bepredicted by matching the current tire tread value with a subset of thedistribution of tread values, and estimating future tire wear based onfurther historical information associated with the matched subset.

Tread values corresponding to the matched values for one or more of theplurality of hierarchically defined parameters may be filtered out ifthe filtered tread values exceed a predetermined boundary, wherein thenumber of unfiltered tread values is compared to the predeterminedthreshold number of available tread values.

The plurality of parameters may be selected based on relevance to tirewear state prediction. The plurality of parameters may be hierarchicallydefined from a highest level to a lowest level based on fastest rates ofpopulation of the corresponding information to slowest rates ofpopulation of the corresponding information, respectively. For example,the highest level of the hierarchically defined parameters may be avehicle fleet location, wherein the lowest level of the hierarchicallydefined parameters may be a particular wheel location for a particularvehicle, and intervening levels between the highest level and the lowestlevel of the hierarchically defined parameters may include one or moreof a vehicle type, a vehicle axle type, and the particular vehicle.

With reference now to FIGS. 5A and 5B, a method 500 as illustrated mayprovide further detail with respect to various embodiments of the method400 associated with FIG. 4 . The method 500 begins (in step 510) with auser collecting tread depth measurements of a tire with correspondingmeta information about that tire (e.g., fleet location, wheel mountposition on the vehicle, vehicle type, vehicle number, vehicle axle,etc.). The term “user” as used herein unless otherwise stated may referto a driver, passenger, mechanic, technician, fleet managementpersonnel, or any other person or entity as may be, e.g., associatedwith a device having a user interface for providing features and stepsas disclosed herein. In an embodiment, the user may provide the metainformation via respective data entry fields, pull-down menus, and thelike in a user interface, but one of skill in the art may appreciatethat various alternatives may be made available within the scope of thepresent disclosure. The tread depth measurements may for example beelectronically captured and submitted, wherein a server or a hostedapplication responds with a user interface including the scanned treaddepth measurement and prompts to enter the meta information, and/orpre-populated fields for confirmation of meta information previouslyassociated with the tire (if any).

Distributions of wear rates may be populated at some or all of aplurality of meta data information levels that are determined to berelevant to tire wear rate estimation. Principal Component Analysis(PCA) may for example be implemented to determine which meta data levelsare most relevant to wear rate, but one of skill in the art maycontemplate alternative mechanisms for determining such levels in viewof relevance to wear rate for a particular tire usage paradigm.

These meta data distributions may preferably be ordered from the highestto the lowest categorical levels, with these levels being determinedbased on, e.g., which wear rate distributions populate the fastest aftertaking inspections.

As represented in FIG. 2 , a plurality of population-level tread weardistributions form an exemplary hierarchy 200 of tread weardistributions moving from high variance to lower variance units. Thelevels in the hierarchy 200 may include, e.g., fleet location 210,vehicle type 220, vehicle type axle 230, vehicle 240, vehicle axle 250,and in some embodiments may include individual tire/wheel location 260.These levels and their corresponding positions in the hierarchy 200 arenot limiting on the scope of a system as disclosed herein unlessotherwise specifically stated, and it may be understood that theselection of levels and their corresponding positions in a givenhierarchical data model may be based on their determined relevance withrespect to tire wear estimation algorithms being implemented, thevariance of corresponding data inputs in a particular system or method,etc.

When a fleet location starts measuring tread depths, the distributionsin the hierarchical model 200 may typically begin populating at thehighest level (e.g., high variance categories such as the fleet location210 and vehicle type 220), and more slowly start filling distributionsat lower and lower levels (e.g., vehicle 240 and vehicle axle 250). Thisaccordingly may create a massive tree diagram of distributions per fleetlocation, wherein every node in the tree may be an aggregation of treadwears with accompanying summary statistics persisted through thedatabase 142. An exemplary node aggregation 300 with accompanyingsummary statistics are illustrated in FIG. 3 , wherein a givendistribution may include four quartiles (Q1, Q2, Q3, Q4) and a Medianvalue, implemented in a manner as further discussed below.

Returning to FIG. 5 , the method 500 further includes, upon readingcurrent tread depth measurements for a given tire into the database(s),calculating/retrieving persisted historical tread wear rates associatedwith the relevant categories for the given tire, e.g., vehicle, axle,wheel position (step 520). The current wear rate for the given tire maybe calculated, with each distribution potentially being filtered toremove ‘unrealistic’ wear rates, such as for example any positive wearchange, or any wear rate that exceeds the bottom percentile of wearrates. For each corresponding level in the hierarchy 200, the number oftread depth observations may be aggregated and a quartile calculated forthe given tire (step 530).

In an embodiment, each distribution may be assigned a minimum samplesize which must be reached before the corresponding statisticalinformation is used to inform the wear rate of a given tire (includingthe tire level). The hierarchies of distributions as disclosed hereinmay generally allow for reasonable tread wear inference to be calculatedat any point in the life of the tire, even for example after a singlemeasurement. However, individual tire tread wear measurements typicallycontain too much variance for the calculations to be reliable until,e.g., five or more suitable measurements are taken over a sufficientwindow of time. Accordingly, whenever a series of logical conditions arenot met for a tire's tread wear measurements (e.g., a predetermined wearrate sample size has not been observed for a given hierarchical level,missing mileage, growing tread, etc.), the system may ‘climb thehierarchy’ to find a suitable wear rate for the given tire (step 550).Each level of the tree diagram (see, e.g., FIG. 2 ) may includeadditional logic that a distribution must meet before influencing thecalculated current tread wear for the given tire. The distributions maynot solely determine the tread wear, but may be implemented to influencethe tread wear calculation with varying degrees of weight. The systemmay for example continuously analyze these distributions to determine orrefine the reasonableness of its own calculations, such that the tire,vehicle axle, or vehicle may preferably never display a tread wear ratethat is objectively unreasonable with respect to wear rates for a givenfleet location or vehicle type.

Referring to the exemplary hierarchy 200 in FIG. 2 , the system ‘climbs’or proceeds to analyze the next most detailed information level above acurrent level. For example, the system may first look (step 551) to thewear distribution 250 for a specific vehicle axle. If the distributionat this level has previously received and aggregated enough tread depthmeasurements to, e.g., exceed a predetermined sample size criterion, themethod proceeds to compare the current tread wear rate for the tire tothe quartiles associated with the relevant node, and to further set thepredicted tire wear rate to the nearest quartile from the distribution.

If the distribution for the specific vehicle axle 250 has not previouslyreceived and aggregated enough observations to create a reliabledistribution, the system and method may proceed one level higher andanalyzes the distribution 240 corresponding to the vehicle upon whichthe given tire is mounted at that current moment (step 552). As with thepreceding step, if the distribution at this level has previouslyreceived and aggregated enough tread depth measurements to, e.g., exceeda predetermined sample size criterion, the method proceeds to comparethe current tread wear rate for the tire to the quartiles associatedwith the relevant node, and to further set the predicted tire wear rateto the nearest quartile from the distribution.

With repeated failures to identify sufficient numbers of observations(or other criteria), the system continues to successively climb thehierarchy and considers the distributions for the axle 230 for that typeof vehicle, that vehicle type 220, and then finally the fleet leveldistribution 210 (steps 553, 554, and 555).

As the system proceeds upwards along the hierarchy 200, the wear rate atthat tire level inspection may be saved and used as a predictiveindicator (sway) at the first appropriate hierarchy level that meets thesample size criteria. The ‘sway’ is created by matching the current wearrate for a given tire with the nearest quartile calculated on thedistributions at the closest appropriate level. That quartile-matchedwear rate, at the closest possible distribution to the tire in view ofthe associated logic and ‘reasonableness’ boundaries, is then used asthe predicted wear rate for the tire.

One exemplary advantage to the methodology as disclosed herein, forexample by reference to FIGS. 4 and 5 , is that once a fleet has beenobserved for several months in consistent intervals, almost all of thedistribution levels have been adequately populated. Each successiveinstance thereafter, when a new tire is placed in any given wheelposition in a fleet, accurate wear information may be predicted aboutthat tire by only taking, e.g., one or two tread measurements. Thisimprovement drastically speeds up the time it takes to provide predictedwear out rates for a fleet inspection.

In various embodiments, for example by continued reference to FIG. 4 ,the method 400 may further involve predicting wear values at one or morefuture points in time, wherein such predicted values may be compared torespective threshold values 465. For example, a feedback signalcorresponding to the predicted tire wear rate (e.g., predicted treaddepth at a given distance, time, or the like) may be provided via aninterface to an onboard device associated with the vehicle itself, or toa mobile device associated with a user, such as for example integratingwith a user interface configured to provide alerts ornotice/recommendations that a tire should or soon will need to bereplaced (step 460). Other tire-related threshold events can bepredicted and implemented for alerts and/or interventions within thescope of the present disclosure and based on predicted tire wear,including for example tire rotation, alignment, inflation, and the like.The system may generate such alerts and/or intervention recommendationsbased on individual thresholds, groups of thresholds, and/ornon-threshold algorithmic comparisons with respect to predeterminedparameters (step 470).

As another example, a hierarchical wear model as disclosed herein mayenable fleet management systems to track performance of not onlyspecific vehicles and tires, but associated routes, drivers, and thelike. Using the predicted wear rates obtained via the methods herein, afleet manger may for example ascertain which trucks, drivers, routes,and/or tire models are burning through tread the fastest, or conversely,saving tread. Furthermore, accurate wear modeling may preferably providedecision support with respect to fleet tire purchasing. Wear outprediction may for example be aggregated into a projected tire purchaseestimation model for a given year, month, week, or the like.

As another example, an autonomous vehicle fleet may comprise numerousvehicles having varying minimum tread status values, wherein the fleetmanagement system may be configured to proactively disable deployment ofvehicles falling below a minimum threshold. The fleet management systemmay further implement varying minimum tread status values correspondingto wheel positions. The system may accordingly be configured to act upona minimum tire tread value for each of a plurality of tires associatedwith a vehicle, or in an embodiment may calculate an aggregated treadstatus for the plurality of tires for comparison against a minimumthreshold.

In an embodiment, a predicted tire wear for one or more future times,distances, or the like may be provided as an output from the model toone or more downstream models or applications (step 480). For example, apredicted tire wear status (e.g., tread depth at a given mileage) may begenerated as feedback or feed-forward signals to a vehicular controlsystem, a traction model (in the same system or as part of anothersystem functionally linked thereto), and/or another predictive modelassociated with fuel efficiency, durability, or the like. The predictedtire wear (e.g., tread depth) may for example be provided along withcertain vehicle data as inputs to the traction model, which may beconfigured to provide an estimated traction status or one or moretraction characteristics for the respective tire. An exemplary tractionmodel may comprise “digital twin” virtual representations of physicalparts, processes or systems wherein digital and physical data are pairedand combined with learning systems such as for example artificial neuralnetworks. Real vehicle data and/or tire data from a particular tire,vehicle or tire-vehicle system may be provided throughout the life cycleof the respective asset to generate a virtual representation of thevehicle tire for estimation of tire traction, wherein subsequentcomparison of the estimated tire traction with a corresponding measuredor determined actual tire traction may preferably be implemented asfeedback for machine learning algorithms executed for example at thecloud server level.

In one embodiment, the outputs from this traction model may beincorporated into an active safety system, an autonomous fleetmanagement system, or the like (step 490). As previously noted, data maybe collected from sensors on the vehicle to feed into the tire wearmodel which will predict tread depth, and this data may further be fedinto a traction model. The term “active safety systems” as used hereinmay preferably encompass such systems as are generally known to one ofskill in the art, including but not limited to examples such ascollision avoidance systems, advanced driver-assistance systems (ADAS),anti-lock braking systems (ABS), etc., which can be configured toutilize the traction model output information to achieve optimalperformance. For example, collision avoidance systems are typicallyconfigured to take evasive action, such as automatically engaging thebrakes of a host vehicle to avoid or mitigate a potential collision witha target vehicle, and enhanced information regarding the tractioncapabilities of the tires and accordingly the braking capabilities ofthe tire-vehicle system are eminently desirable.

Throughout the specification and claims, the following terms take atleast the meanings explicitly associated herein, unless the contextdictates otherwise. The meanings identified below do not necessarilylimit the terms, but merely provide illustrative examples for the terms.The meaning of “a,” “an,” and “the” may include plural references, andthe meaning of “in” may include “in” and “on.” The phrase “in oneembodiment,” as used herein does not necessarily refer to the sameembodiment, although it may.

The various illustrative logical blocks, modules, and algorithm stepsdescribed in connection with the embodiments disclosed herein can beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, and stepshave been described above generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. The described functionality can be implemented invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the disclosure.

The various illustrative logical blocks and modules described inconnection with the embodiments disclosed herein can be implemented orperformed by a machine, such as a general purpose processor, a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a field programmable gate array (FPGA) or other programmablelogic device, discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. A general-purpose processor can be a microprocessor,but in the alternative, the processor can be a controller,microcontroller, or state machine, combinations of the same, or thelike. A processor can also be implemented as a combination of computingdevices, e.g., a combination of a DSP and a microprocessor, a pluralityof microprocessors, one or more microprocessors in conjunction with aDSP core, or any other such configuration.

The steps of a method, process, or algorithm described in connectionwith the embodiments disclosed herein can be embodied directly inhardware, in a software module executed by a processor, or in acombination of the two. A software module can reside in RAM memory,flash memory, ROM memory, EPROM memory, EEPROM memory, registers, harddisk, a removable disk, a CD-ROM, or any other form of computer-readablemedium known in the art. An exemplary computer-readable medium can becoupled to the processor such that the processor can read informationfrom, and write information to, the memory/storage medium. In thealternative, the medium can be integral to the processor. The processorand the medium can reside in an ASIC. The ASIC can reside in a userterminal. In the alternative, the processor and the medium can reside asdiscrete components in a user terminal.

Conditional language used herein, such as, among others, “can,” “might,”“may,” “e.g.,” and the like, unless specifically stated otherwise, orotherwise understood within the context as used, is generally intendedto convey that certain embodiments include, while other embodiments donot include, certain features, elements and/or states. Thus, suchconditional language is not generally intended to imply that features,elements and/or states are in any way required for one or moreembodiments or that one or more embodiments necessarily include logicfor deciding, with or without author input or prompting, whether thesefeatures, elements and/or states are included or are to be performed inany particular embodiment.

Whereas certain preferred embodiments of the present invention maytypically be described herein with respect to tire wear estimation forfleet management systems and more particularly for autonomous vehiclefleets or commercial trucking applications, the invention is in no wayexpressly limited thereto and the term “vehicle” as used herein unlessotherwise stated may refer to an automobile, truck, or any equivalentthereof, whether self-propelled or otherwise, as may include one or moretires and therefore require accurate estimation or prediction of tirewear and potential disabling, replacement, or intervention in the formof for example direct vehicle control adjustments.

The previous detailed description has been provided for the purposes ofillustration and description. Thus, although there have been describedparticular embodiments of a new and useful invention, it is not intendedthat such references be construed as limitations upon the scope of thisinvention except as set forth in the following claims.

What is claimed is:
 1. A computer-implemented method for vehicle tirewear prediction, the method comprising: aggregating information in ahierarchical data structure regarding historical tread values for aplurality of tires, and respective values associated with the historicaltread values for each of a plurality of parameters that arehierarchically defined from a highest level to a lowest level; providinga current tire tread value from a sensor associated with a first tire,and respective values associated with the current tire tread value foreach of the plurality of hierarchically defined parameters; matching thecurrent tire tread value with information from the hierarchical datastructure corresponding to matching values for one or more of theplurality of hierarchically defined parameters having at least apredetermined threshold number of available tread values; and predictinga tire wear rate for the first tire based at least in part on thematched information from the hierarchical data structure.
 2. Thecomputer-implemented method according to claim 1, further characterizedin that the tire wear rate for the first tire is predicted based atleast in part on the matched information corresponding to a highesthierarchically defined parameter having at least the predeterminedthreshold number of available tread values.
 3. The computer-implementedmethod according to claim 2, further characterized in that: the matchedinformation from the highest hierarchically defined parameter comprisesa distribution of available tread values across a plurality of zones,and the tire wear rate for the first tire is predicted by matching thecurrent tire tread value with a subset of the distribution of treadvalues, and estimating future tire wear based on further historicalinformation associated with the matched subset.
 4. Thecomputer-implemented method according to claim 3, further comprising:filtering out tread values corresponding to the matched values for oneor more of the plurality of hierarchically defined parameters if saidfiltered tread values exceed a predetermined boundary, and wherein thenumber of unfiltered tread values is compared to the predeterminedthreshold number of available tread values.
 5. The computer-implementedmethod according to claim 3, further characterized in that the pluralityof parameters are selected based on relevance to tire wear stateprediction.
 6. The computer-implemented method according to claim 3,further characterized in that the plurality of parameters arehierarchically defined from a highest level to a lowest level based onfastest rates of population of the corresponding information to slowestrates of population of the corresponding information, respectively. 7.The computer-implemented method according to claim 6, furthercharacterized in that the highest level of the hierarchically definedparameters comprises a vehicle fleet location.
 8. Thecomputer-implemented method according to claim 7, further characterizedin that the lowest level of the hierarchically defined parameterscomprises a particular wheel location for a particular vehicle.
 9. Thecomputer-implemented method according to claim 8, further characterizedin that one or more levels between the highest level and the lowestlevel of the hierarchically defined parameters include one or more of avehicle type, a vehicle axle type, and the particular vehicle.
 10. Thecomputer-implemented method according to claim 6, further comprising:predicting a replacement time for the first tire, based on the predictedtire wear rate, as compared with one or more tire wear thresholdsassociated with the first tire.
 11. The computer-implemented methodaccording to claim 10, further characterized in that the one or moretire wear thresholds comprise a tire tread threshold corresponding to agiven wheel position associated with the first tire.
 12. Thecomputer-implemented method according to claim 10, further comprising:generating a vehicle maintenance alert comprising the predictedreplacement time and an identifier for the first tire; and transmittinga message comprising the vehicle maintenance alert to a fleet managementdevice.
 13. A system for vehicle tire wear prediction, comprising: adata storage network having a hierarchical data structure storedthereon, said hierarchical data structure aggregating informationregarding historical tread values for a plurality of tires, andrespective values associated with the historical tread values for eachof a plurality of parameters that are hierarchically defined from ahighest level to a lowest level; a sensor configured to provide acurrent tire tread value for at least a first tire; at least onecomputing device linked to the sensor and configured to further providerespective values associated with the current tire tread value for eachof the plurality of hierarchically defined parameters; and aserver-based computing network comprising computer readable media havinginstructions residing thereon and executable by one or more processors,the server network configured to match the current tire tread value withinformation from the hierarchical data structure corresponding tomatching values for one or more of the plurality of hierarchicallydefined parameters having at least a predetermined threshold number ofavailable tread values, and predict a tire wear rate for the first tirebased at least in part on the matched information from the hierarchicaldata structure.
 14. The system according to claim 13, furthercharacterized in that the tire wear rate for the first tire is predictedbased at least in part on the matched information corresponding to ahighest hierarchically defined parameter having at least thepredetermined threshold number of available tread values.
 15. The systemaccording to claim 14, further characterized in that: the matchedinformation from the highest hierarchically defined parameter comprisesa distribution of available tread values across a plurality of zones,and the tire wear rate for the first tire is predicted by matching thecurrent tire tread value with a subset of the distribution of treadvalues, and estimating future tire wear based on further historicalinformation associated with the matched subset.
 16. The system accordingto claim 15, further characterized in that the server-based computingnetwork is further configured to: filter out tread values correspondingto the matched values for one or more of the plurality of hierarchicallydefined parameters if said filtered tread values exceed a predeterminedboundary, and compare the number of unfiltered tread values to thepredetermined threshold number of available tread values.
 17. The systemaccording to claim 15, further characterized in that the plurality ofparameters are selected based on relevance to tire wear stateprediction.
 18. The system according to claim 15, further characterizedin that the plurality of parameters are hierarchically defined from ahighest level to a lowest level based on fastest rates of population ofthe corresponding information to slowest rates of population of thecorresponding information, respectively.
 19. The system according toclaim 18, further characterized in that: the highest level of thehierarchically defined parameters comprises a vehicle fleet location,the lowest level of the hierarchically defined parameters comprises aparticular wheel location for a particular vehicle, and one or morelevels between the highest level and the lowest level of thehierarchically defined parameters include one or more of a vehicle type,a vehicle axle type, and the particular vehicle.
 20. The systemaccording to claim 18, further characterized in that the server-basedcomputing network is further configured to: predict a replacement timefor the first tire, based on the predicted tire wear status, as comparedwith one or more tire wear thresholds associated with the first tire,wherein the one or more tire wear thresholds comprise a tire treadthreshold corresponding to a given wheel position associated with thefirst tire, generate a vehicle maintenance alert comprising thepredicted replacement time and an identifier for the first tire; andtransmit a message comprising the vehicle maintenance alert to a fleetmanagement device.